A Framework to Determine Prominent Research Topics and Experts from Google Scholar

In today’s digital era, most scholarly publications are made available online. These include the data of a university’s research publications which can be reached through Google Scholar. Determining the prominent research areas of a university and finding its experts is the motivation of this study. Although many people may be aware of the published articles of certain university researchers, however there are little or no information on the main research areas of the university where the researchers belong to. Thus, this study will investigate how the prominent research areas can be determined by implementing Refined Text Clustering (RTC) technique for clustering scholarly data based on the titles of publications. Then, an expert search approach can be used to determine the key players who are the experts in each research cluster. The Expert Finding System (EFS) is proposed by applying statistical analysis based on the total of number researcher’s publications and their number of citations

On Perfect Document Rankings for Expert Search

Expert search systems often employ a document search component to identify on-topic documents, which are then used to identify people likely to have relevant expertise. This work investigates the impact of the retrieval effectiveness of the underlying document search component. It has been previously shown that applying techniques to the underlying document search component that normally improve the effectiveness of a document search engine also have a positive impact on the retrieval effectiveness of the expert search engine. In this work, we experiment with fictitious perfect document rankings, to attempt to identify an upper-bound in expert search system performance. Our surprising results infer that non-relevant documents can bring useful expertise evidence, and that removing these does not lead to an upper-bound in retrieval performance